Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people “on the ground” post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emergency responders are largely interested in finding out what events are taking place so they can properly plan and deploy resources. In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency “event”, such as a hurricane). In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. We first extract noun-verb pairs and phrases from raw tweets as sub-event candidates. Then, we learn a semantic embedding of extracted noun-verb pairs and phrases, and rank them against a crisis-specific ontology. We filter out noisy and irrelevant information then cluster the noun-verb pairs and phrases so that the top-ranked ones describe the most important sub-events. Through quantitative experiments on two large crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we demonstrate the effectiveness of our approach over the state-of-the-art. Our qualitative evaluation shows better performance compared to our baseline.

Proper timing of the purchase of airline tickets is difficult even when historical ticket prices and some domain knowledge are available. To address this problem, we introduce an algorithm that optimizes purchase timing on behalf of customers and provides performance estimates of its computed action policy. Given a desired flight route and travel date, the algorithm uses machine-learning methods on recent ticket price quotes from many competing airlines to predict the future expected minimum price of all available flights. The main novelty of our algorithm lies in using a systematic feature-selection technique, which captures time dependencies in the data by using time-delayed features, and reduces the number of features by imposing a class hierarchy among the raw features and pruning the features based on in-situ performance. Our algorithm achieves much closer to the optimal purchase policy than other existing decision theoretic approaches for this domain, and meets or exceeds the performance of existing feature-selection methods from the literature. Applications of our feature-selection process to other domains are also discussed.